{"id":"W7692735","doi":"10.1007/978-3-319-08786-3_16","title":"Te,Te,Hi,Hi: Eye Gaze Sequence Analysis for Informing User-Adaptive Information Visualizations","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Visualization; Human–computer interaction; Gaze; Task (project management); Information visualization; Eye tracking; Sequence (biology); User interface; Data visualization; Perception; Artificial intelligence; Information retrieval; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006137739,0.0008696661,0.0004247781,0.001395771,0.0002498094,0.001241135,0.0006814198,0.0007183077,0.01291416],"category_scores_gemma":[0.003049271,0.0003598104,0.0003432905,0.001528244,0.0002422028,0.0019593,0.001161836,0.0009038437,0.004959268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001958926,"about_ca_system_score_gemma":0.000316246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001286389,"about_ca_topic_score_gemma":0.001826889,"domain_scores_codex":[0.9997889,0.00004966808,0.00001112427,0.00004764876,0.00008462631,0.0000180345],"domain_scores_gemma":[0.999318,0.000350795,0.00003775141,0.00007603845,0.0001769939,0.00004038849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001954143,0.0000421871,0.001084959,0.0004131283,0.00005009994,0.00009464176,0.0004014888,0.002963792,0.03907506,0.005955278,0.06099012,0.8887339],"study_design_scores_gemma":[0.00008252783,0.0002943775,0.01735079,0.000434217,0.0002529854,0.001286962,0.0007956802,0.533653,0.1816943,0.05671421,0.2071956,0.000245474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01468149,0.003542486,0.9548337,0.0006695065,0.0005472238,0.0001139588,0.00158136,0.01650704,0.007523248],"genre_scores_gemma":[0.1764475,0.003647416,0.7865087,0.0002371112,0.0002540639,0.0002725284,0.003075254,0.003229438,0.02632786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01291416,"threshold_uncertainty_score":0.04320216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03054254974593383,"score_gpt":0.306228998579931,"score_spread":0.2756864488339971,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}